As human populations continue to expand more rapidly than the number of medical professionals, medical professionals become more scarce. Thus, medical professionals have ever-increasing needs to more efficiently serve their growing patient numbers, while maintaining consistent levels of quality and accuracy for the medical care that they provide. Many medical professionals use electronic medical records systems (EMR systems) to aid their practices. EMR systems allow users to electronically record, access, and analyze medical information and treatment orders. EMR systems can streamline data gathering, can bring standardization to the storage and presentation of medical information, can provide consistent access to medical information, and have in some instances been shown to improve accuracy in diagnoses and treatment orders.
Though EMR systems bear some advantages, the systems do not always increase efficiency to degrees that merit the time and cost of building and implementing them. For instance, many such systems have only one or a few centralized points of access—terminals or other computing devices—at which data must be entered and received. Users must often collect data themselves and then enter it into the system, nearly doubling their work. These points of access must also be used to access data. While electronic access is typically faster than sorting through paper files, the data must often be accessed, printed or written, and then delivered or relayed to another medical professional or patient who is not present at the access point. Again, the advantage of the systems over paper methods is only slight, when weighed against the time and cost required to build and implement the systems.
Because of the inefficiencies involved with using centralized points of access, electronic medical systems have rarely been adopted, except for storage purposes. Thus, systems that might support medical professionals, or other users, with medical decision-making have been slow to develop. In the 1970's, systems began to develop, which attempted to integrate clinical decision support with electronic medical records, by flagging errors or symptoms and by suggesting questions, tests, diagnoses or treatments. But again, users could access the systems only after locating one of a certain few designated hardware devices. The user was required to enter information, wait for system suggestions, and then relay the information to others at remote locations. In medical practices, this often frustrated both the medical professional and the patient, by disrupting patient-doctor interactions and the fluid course of business within medical care facilities.
Over time, the systems have become more specialized. But, as expensive and time-consuming as these systems are to build, they are only made more cumbersome by tailoring them to meet the needs of individual users. Medical practitioners, for example, often practice in specialized fields, such as cardiology or pediatric surgery. General practitioners often serve specific patient populations. All practitioners would be helped by tailoring systems to account for the peculiarities of their particular medical field and the history of cases that they have served, while also integrating their individual habits or preferences for routine diagnostic methods, terminology, certain medication types or brands, etc., into the systems. Thus, the current systems are not nearly as efficient, helpful, accurate, or easy to use, as they could be, or as users need them to be.
Some inventions make efforts to make individual, highly-specialized tasks more efficient and precise. These systems often employ learning systems or artificially-intelligent systems to enhance medical services. However, the systems are directed toward specific tasks. Some are only useful only for diagnosing one certain type of cancer; or predicting only the probability of heart attack or stroke, based on probabilities and biochemical markers. Other systems provide data analysis and interpretation, to recommend diagnoses or predict treatment outcomes, based on analytical models. Though a bit more useful than traditional rule-based systems, these inventions still do not learn individual preferences, habits, and case histories of their users and do not serve more than their single specific tasks. Additionally, the systems do not assist in assessing patients. Rather, they operate from a patient record that is established solely by the treatment provider. Thus, their utilities are fundamentally tied to the accuracy of the judgment of the treatment provider. Finally, because of the complex technology and great specialization involved in these systems, they are often monetarily out-of-reach for all but the most successful medical practices, and can hardly be justified for any but the most specialized.
Additionally, medical professionals often benefit from the experience of other medical professionals via consultations, training, treatment guidelines, or supervision. As electronic medical systems are deployed, they will be in a position to both observe the decisions of medical professionals and to dispense guidance to medical professionals. However, current systems do not provide an automated way to base guidance given to one medical professional (such as a medical student or nurse) on the decisions and experience of other medical professionals (such as residents or attending physicians). What is needed, then, is a method and system that learns the decision patterns of individual and groups of medical professionals and uses those decision patterns to guide other medical professionals.
Hence, there is a great need in the medical community for a system that allows users to efficiently enter, access, and analyze medical information, without disrupting patient-doctor interactions or medical facility business; which assists in all stages of medical assessment and treatment; and which is tailored to the particular medical practice or specialty and taking into account the developing habits, preferences, performance, and individual patient histories, of an individual user.